U.S. patent application number 11/373832 was filed with the patent office on 2007-02-22 for reward driven online system utilizing user-generated tags as a bridge to suggested links.
This patent application is currently assigned to The Arizona Board of Regents on behalf of Arizona State University. Invention is credited to Hasan Davulcu, Thomas Duening, Prabhdeep Singh.
Application Number | 20070043583 11/373832 |
Document ID | / |
Family ID | 36992374 |
Filed Date | 2007-02-22 |
United States Patent
Application |
20070043583 |
Kind Code |
A1 |
Davulcu; Hasan ; et
al. |
February 22, 2007 |
Reward driven online system utilizing user-generated tags as a
bridge to suggested links
Abstract
A web site for user suggestions of products, services or other
information. The Suggestor also submits tags with those
suggestions. To the extent subsequent users use the same tags to
access or purchase the user suggestion, the suggesting user will be
rewarded. The present invention also provides mechanisms for
disbursing rewards for "finding-and-buying-thru-tags", ranking
suggestions, enabling various privacy preserving communications and
deal validation mechanisms among shoppers, Suggestors and their
social networks.
Inventors: |
Davulcu; Hasan; (Phoenix,
AZ) ; Singh; Prabhdeep; (Tempe, AZ) ; Duening;
Thomas; (Maricopa, AZ) |
Correspondence
Address: |
TOWNSEND AND TOWNSEND AND CREW, LLP
TWO EMBARCADERO CENTER
EIGHTH FLOOR
SAN FRANCISCO
CA
94111-3834
US
|
Assignee: |
The Arizona Board of Regents on
behalf of Arizona State University
Tempe
AZ
|
Family ID: |
36992374 |
Appl. No.: |
11/373832 |
Filed: |
March 10, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60661187 |
Mar 11, 2005 |
|
|
|
Current U.S.
Class: |
705/1.1 ;
705/319; 705/347; 707/E17.108 |
Current CPC
Class: |
G06F 16/951 20190101;
G06Q 50/01 20130101; G06Q 30/02 20130101; G06Q 30/0282
20130101 |
Class at
Publication: |
705/001 |
International
Class: |
G06Q 99/00 20060101
G06Q099/00 |
Claims
1. A method for providing suggested information, comprising:
storing, at a suggestion portal, suggested information recommended
by a plurality of Suggestors; storing, in association with said
suggested information, at least one corresponding tag provided by a
corresponding one of said Suggestors; and accessing said suggested
information by a subsequent user upon entry of said corresponding
tag or tags by said subsequent user.
2. The method of claim 1 wherein said suggested information is a
suggested link on the Internet.
3. The method of claim 1 further comprising: rewarding said one of
said Suggestors when said subsequent user performs a predefined
action in connection with accessing suggested information suggested
by said one of said Suggestors.
4. The method of claim 1 further comprising: storing an
identification of said Suggestors in association with said
corresponding tag and information.
5. The method of claim 1 further comprising: rewarding said one of
said Suggestors only when said Shopper accesses suggestion
information suggested by said Suggestor using a corresponding tag
suggested by said Suggestor.
6. The method of claim 1 further comprising: obtaining a plurality
of links from affiliates; browsing said suggestion website by a
Suggestor; and recommending one of said links by said
Suggestor.
7. The method of claim 1 further comprising: tracking tags used by
said Suggestor browsing sites other than said suggestion portal;
and recommending a link by said Suggestor during said browsing.
8. The method of claim 1 further comprising: providing a list of
possible tags, related to said corresponding tags, to said
Suggestor; associating with said suggestion information ones of
said possible tags indicated by said Suggestor.
9. The method of claim 1 further comprising; prompting a Suggestor
to submit opinions, reviews, coupons, or other comments or relevant
information related to suggested information.
10. The method of claim 1 further comprising; prompting a Suggestor
to submit additional links related to suggested information
11. The method of claim 1 further comprising; prompting a Suggestor
to select at least one category for suggested information
12. The method of claim 1 further comprising; prompting a Suggestor
to designate individuals or groups of individuals to receive
suggested information
13. The method of claim 1 further comprising ranking said suggested
information on said suggestion website.
14. The method of claim 1 further comprising maintaining the
privacy of said Suggestors by concealing the identity of said
Suggestors from other users.
15. The method of claim 1 further comprising obtaining product
description information from a web page of suggested
information.
16. The method of claim 14 further comprising highlighting relevant
information on said web page by said Suggestor.
17. The method of claim 14 further comprising comparing said
product description information with affiliate catalog information
regarding said product.
18. The method of claim 1 further comprising adding a suggestion
button on a browser of said Suggestor, said suggestion button
accessing a suggestion submission module on said suggestion website
upon clicking said browser button by said Suggestor.
19. The method of claim 1 wherein said suggested information is
provided to said suggestion portal over a communication link other
than the Internet.
20. The method of claim 19 wherein said communication link is one
of a telephone network or a satellite network.
21. The method of claim 1 wherein said subsequent user accesses
said suggestion portal over a communication link other than the
Internet.
22. The method of claim 1 further comprising: inputting information
regarding products in a physical store using a mobile device; and
comparing said information with suggestion information on said
suggestion portal.
23. The method of claim 22 further comprising: inputting said
information through one of bar-code scanning, voice activation
entry of tags or keypad entry of tags.
24. An apparatus for providing suggested links over the Internet,
comprising: a suggestion server; a storage device coupled to said
suggested server to store suggested links recommended by a
plurality of Suggestors; said storage device storing, in
association with each of said suggested links, at least one
corresponding tag provided by a corresponding one of said
Suggestors; and a software program stored on computer readable
media providing code for accessing one of said suggested links for
a subsequent user upon entry of said corresponding tag by said
subsequent user.
25. The apparatus of claim 18 further comprising: a reward module,
coupled to said server, configured to reward said one of said
Suggestors when said subsequent user performs a predefined action
in connection with accessing a link suggested by said one of said
Suggestors.
26. The apparatus of claim 18 further comprising: a tracking
module, added to a browser of said Suggestor, for tracking tags
used by said Suggestor browsing sites other than said suggestion
website; and a recommendation module, associated with said tracking
module, for enabling the recommending of a link by said Suggestor
during said browsing.
27. A method for providing suggested links over the Internet,
comprising: storing, at a suggestion website, suggested links
recommended by a plurality of Suggestors; storing, in association
with each of said suggested links, at least one corresponding tag
provided by a corresponding one of said Suggestors; accessing one
of said suggested links for a subsequent user upon entry of said
corresponding tag by said subsequent user; rewarding said one of
said Suggestors when said subsequent user performs a predefined
action in connection with accessing a link suggested by said one of
said Suggestors; storing an identification of said Suggestors in
association with said corresponding tag and link; obtaining a
plurality of links from affiliates; browsing said suggestion
website by a Suggestor; recommending one of said links by said
Suggestor tracking tags used by said Suggestor browsing sites other
than said suggestion website; and recommending a link by said
Suggestor during said browsing.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application claims priority from provisional
application No. 60/661,187, entitled "A Reward-Driven
Suggestion-Portal Creation and Management Method for Online
Products and Services," filed on Mar. 11, 2005.
BACKGROUND OF THE INVENTION
[0002] The present invention relates to the organization of user
suggestions of products, services and information on the
Internet.
[0003] An Internet shopper can search for a desired product, for
example running shoes, by entering keywords ("tags") into a search
engine, such as Google or Yahoo, or by sifting through articles or
blogs that mention running shoes and other relevant material. If
the shopper knows a particular store, the shopper can search the
site of that store. Also, the shopper can search online malls for
products from multiple affiliated stores.
[0004] Internet shoppers can also use a software program or agent
known as an Internet robot or `bot`, or a web crawler (a crawler is
described, for example, in U.S. Pat. Nos. 6,785,671 and 6,714,933)
to conduct searches. Another source of products is online auctions.
Some online auction sites allow shoppers to enter feedback about
sellers. Shoppers can also read product reviews at review sites,
such as epinions or Amazon. Some online malls link shoppers to
these product-specific reviews. Shoppers can post reviews and
comments about products on the Internet. Newsgroups have
traditionally been used by shoppers to post comments about various
products.
[0005] U.S. Pat. No. 6,405,175 shows individuals making suggestions
about products, with hyperlinks to those products. Rewards for the
person making a suggestion are based on subsequent click throughs.
Revenue and rewards generated by click throughs is shown to be
vulnerable to click-fraud (in which unscrupulous competitors create
programs to click on ads repeatedly and cost an advertiser more
money).
[0006] Yub.com teaches users to post suggestions on their own
profile pages (own web pages). This is described in US Published
applications 20050234781, 20050203801, 20050160094, and
20050149397.
[0007] Beenz.com, mypoints.com and Amazon's mturk.com prescribe
work defined by tasks, such as reading emails, visiting web sites,
enriching product information or associating images with addresses.
They compensate the completion of such piece-wise activity with
cash or reward points that are redeemable as discounts or free
products or services. This is described in US published application
no. 20040073483 (see also Beenz.com published application no.
20020082918).
[0008] Fatwallet, Shopping.com, E-Bates and a number of other
comparison shopping sites provide cash-back incentives which are
activated only when a user purchases a product through one of these
sites.
[0009] Amazon provides a review submission mechanism, on their list
of offerings, but they do not provide any reward mechanism for
reviewers. Epinions also provides a review submission mechanism for
its own list of posted products and a reward mechanism, named
"income share", for reviewers. The income share pool is a portion
of Epinions' income. The pool is split among all authors based on
how often their reviews were used in making a decision (whether or
not the reader actually made a purchase). Income Share is
determined by a formula that automatically distributes the bonuses.
The exact details of the formula must remain secret in order to
limit attempts to defraud the system. Users have no direct means of
sharing their alternative product suggestions nor are they rewarded
for their suggestions from outside the e-opinions portal.
[0010] Microsoft has a family of applications which describe
putting software on the desktop and capturing recommendations in
email and documents, and rewarding on that basis (US published
application nos. 20020007309, 20020029304, 20020035581, 20020087591
and 20020198909). The applications describe the "smart tags" used
in Microsoft Office. The software parses the data in a document or
email and annotates it with the relevant URLs. E.g., IBM mentioned
in a word document automatically becomes a link to www.ibm.com.
These smart tags are basically treated as cookies to track the
users. The more cookies someone has (of a particular site) the
better candidate he/she is for a promotion. So if a person has a
document that mentioned IBM 20 times it amounts to advocating IBM
and the author will be offered incentives by IBM.
[0011] In order for the Microsoft software to recognize a concept
type and tag it correctly, it must have some prior domain knowledge
(e.g., it must recognize that IBM has a website ibm.com). It may be
sufficient for Microsoft Office to capture a finite number of
office related concepts, however for suggestions of deals on the
Internet, by definition many of those sites will be new and it is
not practical to include them in a program.
[0012] A number of recent "social media" web companies offer up to
100% of the ad revenues generated from web pages that contain user
contributions on any topic. Examples are:
[0013] Newswine--This site allows readers to create their own
Newswine web pages on a specific topic. Readers submit their own
written stories or become editors by creating their own Newswine
pages on a specific topic. Participating contributors and editors
keep 90% of ad revenues generated by their pages.
[0014] Squidoo.com--This site allows users to create aggregated web
pages, called "lenses", on any topic. Lenses contain user profile
information. Participating contributors and editors will get to
keep 100% of ad revenues (and click through and affiliate income)
generated by their web pages.
[0015] Clipfire.com--This site allows users to submit affiliate
links and earn affiliate income.
[0016] Kaboodle.com (similar to Wists.com and Yahoo's
Shoposphere)--This site is a free social book-marking service. It
was introduced in fall 2005. After registration, a user can
download a button to her browser from the Kaboodle web site.
Whenever the user clicks on the button, a segment of the content
from any web page is automatically identified as the product
information. This automated capture mechanism may yield inaccurate
or incomplete product information. The user is then asked to
manually enter tags and a review. A user's suggestions are then
listed from her public profile and made available to others using
standard keyword search. No reward mechanism is provided for the
users.
BRIEF SUMMARY OF THE INVENTION
[0017] The present invention provides in one embodiment a mechanism
for users to suggest products, services or other information. The
tags or groups of tags that the user (Suggestor) used to find the
suggestions are captured and stored. Subsequent users who use the
same tags will access the Suggestor's suggestion.
[0018] In addition to products, services, and other information,
Suggestors may also suggest bundles of products (such as products
required for a "romantic picnic"), bundles of services (such as
"home repair specialists"), bundles of products and services (such
as "Genie garage door openers and installers"), bundles of products
and information (such as Hoover vacuum cleaners and product
reviews), bundles of services and information (such as a local
plumbing contractor and reviews of its service), or other
information to a plurality of other Internet users for the purposes
of earning cash or other types of rewards. In one embodiment the
invention inextricably connects the Suggestor, tags, and a specific
online link to a product, service, or other information. The
invention automatically tracks the search terms (tags) the
Suggestor used to find the item of interest (product, service, or
other information) on the Internet. The invention also provides a
mechanism for the Suggestor to upload the product or service item
of interest to a web-based "suggestion portal." The invention also
prompts the Suggestor to submit additional search related tags with
a particular suggestion. When subsequent users (Shoppers) who visit
the suggestion portal use the same tags to access and purchase or
otherwise act upon the Suggestor's suggestion, the Suggestor will
earn a reward that is a pre-defined percentage of the commission
generated from the Shopper's actions.
[0019] The invention addresses the problems of Shopper difficulties
with keyword searches when looking for a product, or looking for a
service or other information on the Internet. It is often difficult
for shoppers to determine the best keywords ("tags") to use to find
desired items. Often, search results come back with literally
thousands or millions of hits to sort through. The present
invention essentially captures a "word of mouth" suggestion,
combined with the tags another user (the Suggestor) had initially
tried. In one embodiment, even if the tags aren't the ones that
eventually located the product, the tags are associated with the
product because that is what the Suggestor tried first, and likely
are what Shoppers would try first. As this system tracks and stores
the Suggestor's original tags, and prompts the Suggestor to add
additional intuitive tags, the online Shopper has an improved
chance of identifying desired products, services, or other
information, and doing so more quickly, than existing search tools
and methods allow.
[0020] In one embodiment, the Suggestor is informed if the
suggestion has already been made by another Suggestor, in which
case the Suggestor will not receive rewards. However, if the
Suggestor provides new tags associated with the product, the
Suggestor will receive rewards to the extent those tags are
actually used by Shoppers to locate a product, service or other
information within the suggestion portal.
[0021] The invention also provides a word of mouth, or viral,
marketing system. The suggestions can spread through social
networking on the web. Essentially, the system assembles users into
a collection of loosely federated salesmen for affiliated vendors,
thus contributing to online "hubs of influence" to increase the
traffic and conversion at affiliated vendors.
[0022] The present invention also provides mechanisms for
disbursing rewards for "finding-and-buying-through-tags", ranking
suggestions, enabling various privacy preserving communications and
deal validation mechanisms among Shoppers, Suggestors and their
social networks.
[0023] The present invention in one embodiment provides the ability
to incorporate any ad hoc or new category on the fly for shopping
applications. In many realistic domains, such as shopping, there
will be a variety of new items and categories introduced to
Shoppers. In creating and uploading suggestions, anything that
catches a user's attention that has a market is a fair game for
tagging. Software is limited in dealing with ad hoc categories in
arbitrary domains, and thus the present invention takes advantage
of a human selected tag with a reward mechanism as an incentive for
the Suggestor.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] FIG. 1 is a diagram illustrating the overall operation of an
embodiment of the invention.
[0025] FIG. 2 is a diagram of a suggestion button added to a
browser according to an embodiment of the invention.
[0026] FIG. 3 is a diagram of an embodiment of a page displayed
when a user's tag search directly matches a popular tag.
[0027] FIG. 4 is a diagram of the software modules in suggestion
software according to an embodiment of the invention.
[0028] FIG. 5 is a block diagram of the suggestion website software
according to an embodiment of the invention.
[0029] FIG. 6 is a diagram of an embodiment of the interaction of
Suggestors and Shoppers over the Internet with the Suggestion
website.
DETAILED DESCRIPTION OF THE INVENTION
Overview
[0030] FIG. 1 illustrates the operation of an embodiment of the
present invention. A Suggestor 10 searches on the Internet for
products 12 using tags 14, 16. The Suggestor can search in various
ways, such as using online shops 18 or search engines 20. The
Suggestor tries various tags and looks at various links until a
relevant product, service, or other information is discovered. The
product, service, or other information can be relevant based on
price, features, or other aspects.
[0031] The Suggestor then submits the deal (product or service) as
a suggestion to the suggestion web site 22 thru suggestion portal
24. The suggestion 26 includes 3 components: (1) a link to the
suggested product/service, (2) the relevant tags used in the
searches to find the link, and (3) an identification of the
Suggestor. The suggestion with its tags is compared to existing
suggestions. If the link is new, or if there are new tags for an
existing link, the suggestion is accepted and stored in a memory
28.
[0032] A Shopper 30, a subsequent user, will visit the suggestion
portal web site 22. If the Shopper uses a tag submitted by the
Suggestor to purchase a product/service at a link provided by the
Suggestor, rewards 32 will be provided to the Suggestor 10.
[0033] An embodiment of the invention may be implemented as a
suggestion portal which comprises (1) a browser or a desktop
component that tracks tags and enables submission of a suggestion.,
sometimes called the client suggestion module herein and (2) a
suggestion website on a server connected to the world-wide-web or
Internet. The suggestion web site contains a wide variety of
products with their commercial and technical attributes. The client
suggestion module is software that aids in the development and
submission of suggestions by Suggestors.
Tracking Tags
[0034] The client suggestion module enables a Suggestor to
electronically submit a suggestion and all associated tags, by
simply highlighting the product information on any web page and by
selecting or inputting product associated descriptive tags. The
client suggestion module can be disseminated and invoked by any
kind of electronic media.
[0035] FIG. 2 shows one embodiment of a user browser toolbar 40
which includes a suggestion button 42. Button 42 is downloaded with
tracking software for assisting a Suggestor. The software will
track the URLs, or links, visited by a Suggestor using the browser.
The tags used by the user in the search are recorded, including
tags in the links themselves. In particular, the grouping of tags
used in a search is recorded. When the user settles on a link and
wishes to make a suggestion, the user simply clicks on suggestion
button 42.
[0036] When suggestion button 42 is clicked, it will record the
present webpage as the link for the suggestion. It will associate
with that link all the tags used in the search. Additionally, it
will associate information identifying the Suggestor. These 3
elements together form a suggestion component, which is then
uploaded to a suggestion website as illustrated in FIG. 1.
[0037] The software can also scrape information from the webpage
being submitted to identify the product, service or information on
the website. For example, it may record commercial and technical
attributes. Commercial attributes may include information such as
product name, product URL, image URL, price, description, brand,
category and availability. Technical attributes may include
detailed specifications of technical product features such as size,
weight, color, material. This data may be presented to the
Suggestor to verify first, or may be automatically uploaded.
Alternately, the Suggestor may highlight parts of the webpage to
include, and the software can include the highlighted information
in addition to, or instead of, the automatically captured
information.
[0038] The highlighted or automatically captured product
information can be checked against the product catalog of the
affiliated vendor. If a match is found in the product catalog of
the vendor then the more accurate and complete product information,
such as the product name, description, image, brand, price, SKU
number, affiliate link and other technical attributes, is used as
the product information rather than the user highlighted or
automatically extracted information available on the web page. This
allows a later parametric search by brand, price and the other
attributes captured. User highlighted or automatically extracted
information can be incorrect and incomplete since a user may not
highlight an important piece of information or the extraction
algorithm may select the incorrect or incomplete segments of
information from a web page.
[0039] In another embodiment, a whole toolbar may be added to the
user's browser instead of just a single button. The additional
buttons can activate different features. For example, one button
could link to the suggestion website. Another button may bring up a
list of prior, incomplete searches that did not result in a
suggestion. The toolbar may report in real time the total rewards
earned from a user's suggestions, it may also contain a button to
the detailed earnings report. The toolbar might contain an
indicator that a shopper may need the assistance of a Suggestor
based on a tag or product suggested by the Suggestor. The toolbar
may also display other online users so that Shoppers may
communicate among themselves or with the online or offline
Suggestors using real time communication mechanisms such as instant
messaging, VOIP or regular telephone calls with or without
co-browsing, or asynchronous communication mechanisms such as
anonymous email, message boards or voice mail exchange. The toolbar
may also be used by Shoppers or Suggestors to retrieve deals from
the suggestion web site while shopping online at another web site.
In this case the suggestion web site may be used as a validation
authority for ensuring the soundness of a deal found online or
offline at another shop.
[0040] In another embodiment, instead of a button or toolbar, a
cookie is loaded onto the Suggestor's computer. The cookie records
the tags and associated links during searching by the Suggestor. A
separate cookie could be provided for each of a number of key
websites. When the Suggestor subsequently returns to the suggestion
website, the cookie can be inspected to determine the tags and
link. The link can then be visited to capture the desired
commercial and technical information. Alternately, any other method
of tracking tags and links may be used.
[0041] In another embodiment, the Suggestor first visits the
suggestion website, and from there pulls up the site of a search
engine, shopping mall, merchant site or other website for searching
for products, services or information. The suggestion website then
tracks the Suggestor's tags and links. In this, and the above
methods, the tracked information may be discarded if the Suggestor
submits a corresponding suggestion within the same session or
within some time period. Alternately, the information may be saved
until the Suggestor indicates it should be deleted, such as by
allowing a Suggestor to save an incomplete search for another
day.
[0042] In another embodiment, instead of, or in addition to, the
above options, the Suggestor can cut and paste or type in
suggestion information from a vendor site, including tags, links
and product information into a form available in the suggestion web
site. The Suggestor will be prompted to arrange the tags in groups
that would be used in a search, not just input them separately.
[0043] Online users spend significant time and effort to find
matching items for their needs. Following the 80/20 rule, it has
been noted that 80 percent of web users search for 20 percent of
searched items. The terms people type into Internet search engines
everyday are called tags. There are thousands of tags that are used
by Internet users for querying search engines. Popular tags are
sets of search phrases that are frequently used to search. Popular
tags are typically 2 to 3 word phrases. For example, in conducting
searches online for specific products some popular tags might be:
portable humidifiers, red rugs, garden lighting, running shoes, GE
dishwashers, high efficiency washers, etc.
[0044] When entered into a search engine, tags generate returns
based on the algorithms used by the particular search engine. No
two search engines will produce the same returns. There currently
is no automated solution that produces only high relevance results
for even the popular tags. Normally an individual using a standard
Internet search engine will try various different tags phrases and
follow various "links" before locating a matching product or
service. People searching for items while shopping online fare no
better than anybody else when trying to generate relevant results
using standard search engines. For example, a person who enters a
tag such as "garden lighting" will generate thousands of resulting
"hits", but there is no quick or automatic way to sort the list for
personal relevance. The ideal would be to find the entire garden
lighting range of products available on the web in one location,
and also be able to determine which garden lighting items other
people are buying and their pre-sale and post-sale experiences.
[0045] This invention provides a method for Suggestors to register,
publish and share their findings with a plurality of online
shoppers. Product associated tags are identified by the Suggestor
using the client suggestion module. The client suggestion module
automatically creates a list of candidate tags as a result of
recording a Suggestor's online searching interactions, such as
his/her tag search phrases, URLs for the links that he/she visits,
and other tag phrases during form fill-outs--at various search
engines and vendor web sites. The client suggestion module can also
query various other relevant tag or phrase databases. Users can
formulate various tag searches against those databases to retrieve
additional relevant tags for their product suggestion.
[0046] The suggestion web site can also be searched using the
client suggestion module without visiting the suggestion web site.
Upon a Shopper's request, the client suggestion module can be
configured to automatically search the suggestion web site with
user determined tags or parametric searches and return relevant
matches and suggested items in real time. If the Shoppers tag
search directly matches a popular tag associated with suggested
products by various Suggestors then the suggestion web site will
return to the Shopper the corresponding group of suggested
products, their associated reviews and other suggested related
tags. This tool can be provided to both the Suggestor looking for
product deals, and to a Shopper.
[0047] FIG. 3 shows an embodiment of the user interface presented
to the Suggestor. A tag 90 is displayed along with a suggested
product list 92, a group 94 and forum messages 96. The suggested
product list will display all products associated with tag 90. A
hyperlink 98 enables browsing through the whole list of suggested
products.
[0048] Group section 94 displays suggested accessories 100 and
suggested related products 102. One or more hyperlinks 104 provide
a link to those related product and accessory pages. Forum messages
section 96 displays reviews and comments by other users, with one
or more hyperlinks 106 providing a link to those pages.
[0049] Also provided is a button/toolbar download area 108.
Suggested related tags 110 are presented. Finally, the page can
include ads and banners 112 that are related to the tag 90 or any
other information on the page or linked pages.
[0050] The client suggestion module can be enabled or disabled upon
a user's request. The highlighting based registration mechanism
built in the client suggestion module ensures that only valid
product information from shopping or services web sites can be
posted as suggestions.
Suggestion Components
[0051] As noted above, a suggestion has 3 components:
[0052] (1) Link. The link to the product, service or other
information. In different embodiments, this also includes
commercial and technical information. The link could be input by
any of the means discussed above. The Suggestor can highlight
additional information from the webpage to include in the
corresponding suggestion, and/or the Suggestor can enter additional
information. Additionally, the Suggestor can correct or modify
information that has been automatically captured by the system.
[0053] (2) Tags. These tags can be captured by any of the means
discussed above. Additionally, the Suggestor may type in or cut and
paste additional tags. Also, the suggestion software can suggest
other possible tags based on the tags, link, or commercial or
technical information submitted. The Suggestor can be given the
opportunity to accept or reject the suggested tags.
[0054] (3) User identification. The suggestion website stores some
sort of ID information to identify the Suggestor. This could be an
email address, so the Suggestor can be notified of earned rewards.
It could be some other unique code, with it being up to the
Suggestor to log on with the code to determine if any rewards have
been earned. In one embodiment, the Suggestor ID information is not
presented with the suggestion to subsequent users, or is presented
in a form which protects the Suggestor's privacy. The Suggestor
identification information can be captured during a registration
process where the Suggestor provides desired information. The
registration could be done when the Suggestor first visits the
suggestion website, or could be done by prompting the Suggestor at
the time of the first suggestion submission.
Suggestion Submission, Acceptance
[0055] FIG. 4 is a diagram of the suggestion acceptance software
modules or elements. A suggestion is submitted by the Suggestor,
with the suggestion components, as described above. In one
embodiment the suggestion software also performs the following
functions:
[0056] Comparison to previous suggestions. The suggested link and
tags are compared to previously suggested links and tags by a
comparison module or engine 50. The Suggestor is informed if the
suggestion has already been made, or if some or all of the tags
have already been suggested.
[0057] Additional recommended tags. Additional possible tags are
presented to the Suggestor by a recommended tags generator module
52. This allows the Suggestor to decide whether to add those tags
to his/her suggestion. This represents an improvement over prior
art which describes the automatic adding of tags Automated systems
are not good at determining tag relevance to actual shoppers. The
task of determining relevance is still best done by people acting
in their own self interest. The additional tags can be generated in
any number of ways. For example, the tags provided by the Suggestor
can be compared to other tags for other existing links on the
suggestion website. If there is a match, the other tags for that
existing link are presented to the Suggestor. The Suggestor can
then decide whether to add them to the suggestion. Additionally,
the software can query a tag or phrase database that stores related
words and phrases, and present those to the Suggestor. The
Suggestor can also formulate searches of those databases to try to
find additional tags. The Suggestors can thus hyperlink their tags
to other related tags so that their suggestions can be found from
other relevant categories. This leads to a rich and useful linking
within the suggestion website.
[0058] If a certain tag has already been associated with the user's
suggested product then it is not accepted as a suggestion, since
duplicate tags are not allowed. For example, a user might search
"oriental bedding" at a search engine, and then might get to a page
where he/she clicks on a link with a label "Chinese bedding" to
reach a set of products. If the user finds a matching product that
he/she likes then she can use the client suggestion module to tag
that product with both "oriental bedding" and "Chinese bedding". If
"oriental bedding" has already been suggested, it won't be
accepted, but "Chinese bedding" will be accepted. The client
suggestion module can also be used to retrieve additional tags
matching "bedding". Upon eyeballing the tags matching "bedding",
the user might identify additional relevant tags such as "Asian
bedding" and "blue bedding" or "blue Asian bedding".
[0059] Tag Dissemination. In one embodiment, tags on the suggestion
website can be disseminated. An RSS (real simple syndication)
mechanism is built into every page so that it is easy for users to
get updates on the pages they would like to track. For example, a
user can subscribe to a feed on the "titanium woods" page so that
as soon as anyone makes a change on that page it is pushed into the
user's browser or other device such as a cell phones. The RSS
mechanism can also be used to "market" tag pages into Technorati
and other social networking platforms. This allows the suggestion
website pages to be easily incorporated into the blogs that would
like to talk about them.
[0060] Related products and other links. A recommended links module
54 can recommend possible related hyperlinks to the Suggestor in
the same manner as the recommended tags. Links previously related
to similar links or tags can be presented to the Suggestor, to
accept or reject. Again, this allows human review of the
automatically generated possible links. Alternately, links could be
automatically generated or added by administrative personnel. The
Suggestor could search the suggestion website for possible related
links, reviewing recommended links and search terms in the process.
In addition to links to related products, other relevant
information that might be of interest to a future buyer may be in
the same or alternate manners. For example, an additional
information module 56 may assist the Suggestor in generating
shopping tips, topic or group names, coupons, deals and URLs or
other relevant web pages and user reviews.
[0061] The Suggestor has the option of associating a suggestion
with any category, tag or topic of his/her selection within the
suggestion portal to increase the findability of the suggestion.
The suggestion-portal may also automatically place a user's
suggestion under other relevant categories.
[0062] Suggestor privacy. A Suggestor privacy module 58 in one
embodiment provides the Suggestor with options regarding the
Suggestor's identity. Maintaining the Suggestor's privacy is the
default mode. The suggestion portal maintains the privacy and
anonymity of the Suggestor from other portal visitors. Alternately,
the Suggestor may elect to have the Suggestor's name or a pseudonym
used. This would be of value where a particular Suggestor
establishes a reputation which will enhance the likelihood of
Shoppers purchasing the Suggestor's products, services, or other
information.
Ways of Taking Suggestions
[0063] The suggestion can be taken by an automatic system wherein a
user is shown a collection of products/services/content and the
collection is taken as a suggestion. The suggestion is taken
explicitly or implicitly by multiple means and sources. The
Suggestor can click on a suggest link on a product shown in the
suggestion website system of sites and/or give some information
explicitly to register a suggestion. The Suggestor can simply
browse through the products in the suggestion website and his/her
browsing actions can be taken as suggestions. The suggestions can
be taken in the form of a URL supplied by the Suggestor. The
suggestion software automatically extracts the product attributes
when supplied a URL as the suggestion. The Suggestor is shown the
extracted attributes and properties for validation once they are
extracted from the suggested page. The suggestion can also taken
directly from the toolbar/button in the web browser of the
Suggestor. The suggestion can be made from multi-modal and multi
form factor devices like pocket PCs, cell phones, voice activated
systems, kiosks etc.
[0064] Other Suggestion Software Features The software is activated
when the user wants to give a suggestion. The suggestion software
can be a server based (web based) system wherein the user does not
have to install the client software in which case the user will
have to explicitly supply information like URL, etc. and the
software is activated by a frames or activeX based system in the
user's web browsers. The software (client or server based) is a
system which extracts the attributes of the product/service or
content and shows them to the user explicitly for validation. If
the suggestion website has an affiliate relationship with the
suggested vendor/provider it informs the user of the compensation
structure for the user in case his/her suggestion performs.
Affiliates
[0065] In one embodiment, for a Suggestor to be able to share in
revenue, the link must be that of an affiliated vendor. An
affiliated vendor is a qualified product, service, or other
information vendor that has agreed to make a referral payment to
the suggestion portal when a Shopper is referred by the portal to
the vendor's products, service or other information. The referral
payment can be based on actual purchases, clicks, or any other
measurement mechanism. If the link is owned by someone that is not
already an affiliate of the suggestion website, the acceptance of
the suggestion is conditioned on the subsequent enrollment of the
owner as an affiliate.
[0066] In one embodiment, the suggestion website signs up
affiliates and has a data feed to populate the suggestion website
with the product links of the affiliates. The product technical,
commercial, and other information can be obtained as data feeds
(ftp, email, downloads) in various formats from online vendors.
Suggestors can then browse the suggestion portal to determine which
of those links to suggest. Links that are suggested are marked or
highlighted in some manner, so that future users can see that
someone has suggested this product. In addition, the suggested
links can be displayed first, or more prominently, for subsequent
queries by other users.
[0067] The suggestion portal provides a medium for hosting,
organizing and sharing users' suggestions with a plurality of
prospective buyers. The suggestion portal provides a variety of
search methods such as--tag search, product or service taxonomies,
and user suggested hierarchies of tags and topics that are
associated with various suggested products and services.
[0068] In an alternate embodiment, a Suggestor can browse other
sites, and then submit links to the suggestion website. The
suggestion website software will determine if the link belongs to
an affiliate and is governed by an affiliate agreement. If not, an
invitation to join the affiliate network may be sent to the link
website, with acceptance of the suggestion conditioned on the
website owner signing up as an affiliate.
Rewards
[0069] The client suggestion module enables a built-in reward
mechanism that operates as follows: If a prospective buyer visits
the suggestion portal and finds and buys an item or does something
useful such as filling out a survey or contact form for a product
or a service through a suggestion related tag or other descriptive
annotation such as a review or comment, then the Suggestor is
rewarded with compensation based upon the revenue derived from each
such sale or action. Alternately, the reward could be derived from
click through action on the suggested link related to a descriptive
annotation such as a review or comment. The suggestion portal and
the client suggestion module can communicate to deliver sale
performance and reward information to the Suggestor.
[0070] The Suggestor could be informed of reward status by email or
any other means. A personalized web page could be established for
the Suggestor, with the suggestion web site software posting
accumulated rewards for each suggestion. The Suggestor can view the
performance of his/her suggestions and potential rewards. The
rewards can be credits, coupons, cash or any other compensation. A
policy may be established so that no reward is paid until a minimum
or threshold amount is reached. The rewards could be tiered, so the
Suggestor gets a larger incentive for more revenue generated.
[0071] A suggestion generates rewards by virtue of its performance
(sales, click-throughs, impressions or otherwise) in different
revenue channels. Any information given by the user which can
generate revenue is a candidate suggestion, and the revenue
generated through it is shared with the user who supplied that
information.
[0072] In one embodiment, the invention uses a unique ID in the
affiliate links as an identifier to track the Suggestor and tag
associated with the product. The post-sales reports from affiliates
reflect these IDs which are used to calculate the performance and
rewards for the Suggestors.
Subsequent User Searching
[0073] Any Web Shopper visiting the suggestion portal can search
the suggestions using a variety of methods, such as tag search or
taxonomy and attribute guided navigation, to find a product or
service offering matching his/her needs. If a Shopper purchases a
suggested item, then the suggestion-portal earns a commission, a
part of which is credited to the item's related Suggestor.
[0074] Ranking. The suggestion portal can rank the matching
products based upon their click through traffic and sales
statistics. The more highly ranked products are more prominently
displayed. Administrative procedures can be implemented so that
underperforming suggestions can be detected and eliminated. The
suggestion portal tracks traffic and sales on each suggestion and
can display appropriate visual information (histograms, gauges) to
aid shoppers in their decisions.
[0075] The ranking can be done using any number of ways, including
ranking by price, by sales, by click through rate, by alphabetical,
by brand or any other technical or categorical dimension. Default
views are provided to the user, with the user being given the
ability to view other ranking methods. The most popular links can
be grouped together in one area. Also, the most popular links
corresponding to a particular tag (tag or tag combination) can be
displayed as default when that tag is entered by a user.
[0076] Alternately, the ranking of products can be based on any
number of factors instead of, or in addition to, traffic and sales.
An algorithm could combine various factors in a way that minimizes
gaming of the system. For example, buyers could rank matching
products based on their Suggestors' past performance, or could fill
out an evaluation of a Suggestor with information on the evaluation
factored into the ranking (or a system like Ebay's personal rating
system). If a Suggestor has a history of suggesting fewer products
that sell a lot, then a buyer might prefer to see that star
Suggestor's freshly suggested product sooner (i.e., before it takes
time to perform). Various ways of ranking could be presented to the
user, with the user being able to select which ranking or ranking
combination to use. Or the Shopper could customize a page so the
Shopper sees favorite Suggestors in addition to overall
rankings.
[0077] In one embodiment, the suggestion portal ranks suggested
products under a tag based on their Suggestors' past performance,
so that a shopper can spot these freshly suggested products sooner
(i.e., potentially before the time it takes for them to perform and
become hot/popular products under this tag). The suggestion website
can also group the products listed under a tag by their Suggestors,
so that a Shopper can see all suggestions of an expert or
like-minded Suggestor altogether. Additionally, users can be given
the ability to see all suggestions of an expert/like-minded
Suggestor under all tags. This would be like exploring the "private
shop" of a Suggestor. Power Suggestors can name their shops as a
perk.
[0078] In one embodiment, the pages of the suggestion website
initially will have the suggestions ranked by their performance.
The user will have the option to set a preferred mode of
presentation. The web pages will include in various embodiments
guides, parametric search input boxes, context breadcrumb links,
guides and other features to enhance usability.
Other features of Suggestion Software
[0079] Suggestor features. The Suggestor can (1) make suggestions;
(2) see if the product is available with the suggestion website;
(3) see a list of the tags relevant to a particular product
suggestion; (4) organize and move related tags; (5) post and
participate in a forum: (6) suggest related products; (7) track
suggestion performance through a detailed report of how much
traffic and conversion the user's suggestions attracted; (8)
register and provide address and related info for receiving updates
and suggestion rewards; (9) send suggestions to individuals or a
closed group of individuals.
[0080] Product listing features. The software for displaying
products can (1) group products on suggestions by their tag; (2)
bring in new products through the data feeds provided by the
vendors; (3) receive new product suggestions from affiliated
vendors; (4) aggregate a list of unaffiliated vendors and products
for processing after taking suggestions from unaffiliated vendors;
(5) set up data-feed processing automation for including products
after a vendor affiliation; (6) track product performance ( traffic
and conversion ); (7) take off discontinued or out-of stock items
after customer flagging or vendor discontinuation; (8) incorporate
new products or refresh existing product data without disturbing
existing rankings; (9) track the pages to get the most sticky hot
pages.
[0081] Orders. The software for handling orders can (1) incorporate
the affiliate network (Commission Junction, LinkShare) reports into
the reporting structure; (2)lntegrate the sales data with the user
suggestion data; (3) provide visibility to the sales reports in
terms of suggestions; (4) incorporate and translate sales into user
rewards.
[0082] Payments. Payment software functions to (1) reimburse users
for their suggestions; (2) set-up the minimum reward thresholds;
(3) establish rewards programs to multiply the potential rewards
for the users; (4) incorporate time delay to account for the
user-returns.
[0083] Advertisers. Advertising software can (1) provide the
advertisers with a comprehensive report of the pages in their
domain; (2) provide a cost model to charge more for the heavy
traffic and high conversion pages; (3) provide the advertisers with
a pricing chart for the phrases they want to target.
[0084] Browser Button. The browser button software provided to a
potential Suggestor can (1) sense the product in a page and its
important attributes; (2) get the data validated by the user match
it with the suggestion database and register the suggestion after
taking in the relevant tags. The browser button software in one
embodiment takes a snapshot of the HTML page DOM model, ad-hoc, on
form post-backs (i.e., when a user fires searches--fills up the
keyword query in search boxes). The software intercepts the data
before it is submitted to the servers. The software inspects the
DOM and extracts the keyword queries to be presented as candidate
tags at the time of suggestion submission.
Suggestion Website Software Modules
[0085] FIG. 5 illustrates one embodiment of the modules of the
software at the suggestion website.
[0086] A Product Data Collection module 60 manages the automatic
collection and aggregation of product data from the affiliated
online vendors. Tags and hot tag phrases are obtained from various
sources such as Google and Word Tracker. Data collection is done
with the help of vendor supplied data feeds and web data extraction
technologies. A Data Cleanup module 62 manages cleaning up the raw
product data from different sources into a uniform data format that
the website uses in subsequent data processing stages. Matching and
Processing module 64 manages preparing data for the `tagged` raw
product pages from which the Suggestors will suggest products.
Keyword matching is used for automatically bootstrapping the
suggestion website tagged product pages. However, tag matching
products are always kept separate from the suggested products.
[0087] Product Data Publication module 66 publishes the raw tagged
pages with the correct hyper linking and taxonomy organization in a
specified design template. Content checking and management module
68 is a set of automated tools given to the data operators and site
maintenance staff to ensure the product variety and relevance on
the suggestion website shopping pages. Traffic Sensing module 70
actively monitors the traffic on the suggestion and dynamically
ranks the suggestion based on their performance. This module
accounts for every click in the suggestion website and is
responsible for extensive user profiling. Suggestion Collection
module 72 handles suggestion collection from the suggestion
website.
[0088] Suggestion Publication module 74 does the data cross
checking, tagging and affiliate check of the suggestion and
publishes it in the appropriate tag page. Forum Management module
76 is responsible for managing the user conversation threads in the
suggestion pages. Suggestion Collection Button on Browser module 78
gives the users a browser button to send the suggestions to the
suggestion website from anywhere on the web. User Data Management
module 80 manages the user information including emails, login IDs,
addresses, alias, etc. User Reporting module 82 is a web based
reporting system for the Suggestors where they can view the
performance of their suggestions and potential rewards. Rewards
Disbursement Management module 84 is responsible for generating the
final reward reimbursement medium (checks, credit vouchers etc).
Rewards Calculation module 86 is responsible for sales data
collection and calculation of rewards based on that data.
Administrative Reporting and Management module 88 is responsible
for generating the revenue reports, administrative data,
performance summaries and strategic reports for power Suggestors
and the suggestion website administrators and marketers.
[0089] Friends family forums: Spheres of influence. A Suggestor can
form different groups to share suggestions with. When the Suggestor
submits a suggestion, it will also be sent to any groups, through
email or any other means, designated by the Suggestor. Even if a
suggestion is rejected as already having been suggested, it can
still be sent to these groups from the Suggestor and the Suggestor
can earn rewards if anyone in their referral network acts on the
suggestion.
[0090] Multi-modal accessibility for deal validation. Each
individual's referral database is accessed via the website
regardless of information channel (home computer, work computer,
web phone). The suggestion website hosts all the services and
suggestions via (a multi-modal) website. Online/offline deals can
be validated through a cell phone or other mobile device. A user
can call the suggestion website with a mobile device while
shopping. The user can text message, or speak to a VRU and tell the
database about the great deal the user just found, or check to see
if other Suggestors think it is a great deal, or have found better
deals.
[0091] Off-line and local suggestions. In one embodiment, a
Suggestor can get rewards for offline purchases or visits. Other
people in the Suggestor's affinity group would get email, voice
mail or a text message letting them know about the deal. Those
group members could then go to the retail store, and tell the clerk
how they were referred. In some embodiments, a coupon would be sent
with the referral, which the person could print and take to the
store. The coupon would have an ID that indicated the Suggestor.
Alternately, the coupon or referral could be in a text message
which could be read at the POS, either with a scanner or
wirelessly. Retail partners that have agreed to this arrangement
would then provide a reward of some type to the Suggestor.
[0092] FIG. 6 is a diagram of an embodiment of the interaction of a
Suggestor at a Suggestor computer 120 and Shoppers at Shopper
computers 122, 124 over the Internet 126 with the Suggestion
website server 128. The Suggestors may also browse, for example, at
an individual site on a server 130 or a shopping mall on a server
132. Associated with suggestion website server 128 is memory
storage 134 for storing the tags, links and Suggestor information,
along with other data described herein. One implementation of a
database on storage 134 is a relational database model to maintain
the relationship data (where the tuples consist of <Key, Tag,
User ID, Item ID>). Alternately, suggestions can be implemented
as an inverted list of tags mapping to the items in the database.
Any other database storage structure could also be used.
[0093] As will be understood by those of skill in the art, the
present invention could be embodied in other specific forms without
departing from the essential characteristics thereof. For example,
the Suggestor could suggest articles or information rather than
products or services. The incentive could be non-monetary, such as
recognition for political volunteers who get the word out and find
favorable coverage for their candidate. Alternately, a salaried
group of Suggestors could be employed.
[0094] Alternately, a Suggestor can make suggestions over mobile
cell phones and other similar devices that do not use the Internet.
For example, they may just dial an 800 number or use a satellite
network etc. Also, a shopper may access the suggestion database
directly using an 800 number, etc., without ever accessing the web
site or internet. Bar-code decoding & comparative shopping can
be done via camera cell phones or voice activation or keypad entry
of tags, allowing a Shopper in a real store to compare items in the
store with suggestions on the database. Off-line and local
suggestions can be posted, and offline users can receive suggestion
information through other channels in order to validate an offline
shopping decision using a variety of communication devices and
networks Accordingly, the foregoing embodiments are intended to be
illustrative, but not limiting, of the scope of the invention which
is set forth in the appended claims.
* * * * *
References